Abstract
Orthogonal frequency division multiplexing (OFDM) is a widely adopted system for broadband wireless transmission, However, time-varying channels degrade the performance owing to the intercarrier interference (ICI). This study discusses the performance enhancement problems of channel estimation and signal detection in view of fast time-varying multipath fading channel characteristics of high mobility. A two-stage method is presented. In the coarse stage, initial channel estimation is performed using the signals at pilot subcarriers based on least-square (LS) estimate is performed. However, the initial estimate still needs to be improved because of the data-induced ICI. The coarse stage improves estimate accuracy by compensating the interferences. The refining stage, applies interference compensation and q-banded successive interference suppression (qSIS) methods to enhance the detection and re-estimate accuracy. Based on robustness, the detected data are called the “pseudo-pilots”, which together with the pilots are treated as a “visual-training” (VT) to improve channel re-estimation. Simulation results indicate that the proposed method is more robust than the traditional method in fast time-varying multipath fading channels with lower computational complexity. The proposed method is independent of the delay and fading properties of the channel.














Similar content being viewed by others
References
Bingham, J. A. C. (1990). Multicarrier modulation for data transmission: an idea whose time has come. IEEE Communications Magazine, 28(5), 5–14.
Schniter, P. (2004). Low-complexity equalization of OFDM in doubly selective channels. IEEE Transactions on Signal Processing, 52(4), 1002–1011.
Stamoulis, A., Diggavi, S. N., & Al-Dhahir, N. (2002). Intercarrier interference in MIMO OFDM. IEEE Transactions on Signal Processing, 50(10), 2451–2464.
Wang, T., Proakis, J. G., Masry, E., & Zeidler, J. R. (2006). Performance degradation of OFDM systems due to Doppler spreading. IEEE Transactions on Wireless Communications, 5(6), 1422–1432.
Morelli, M., & Moretti, M. (2009). Channel estimation in OFDM system with unknown interference. IEEE Transactions on Wireless Communications, 8(10), 5338–5347.
Simon, E., Ros, L., Hijazi, H., & Ghogho, M. (2012). Joint carrier frequency offset and channel estimation for OFDM systems via the EM algorithm in the presence of very high mobility. IEEE Transactions on Signal Processing, 60(2), 754–765.
Mostofi, Y., & Cox, D. (2005). ICI mitigation for pilot-aided OFDM mobile system. IEEE Transactions on Wireless Communications, 4(2), 765–774.
Kwak, L., Lee, S., Min, H., et al. (2010). New OFDM channel estimation with dual-ICI cancellation in highly mobile channel. IEEE Transactions on Wireless Communications, 9(10), 3155–3165.
Hijazi, H., & Ros, L. (2009). Polynomial estimation of time-varying multipath gains with ICI mitigation in OFDM systems. IEEE Transactions on Vehicle Technology, 58(1), 140–151.
Jan, Y. H. (2015). Transmission efficient channel estimation for OFDM System under multi-path fast fading channel. Wireless Personal Communications, 84(2), 1561–1575.
Jan, Y. H. (2015). Efficient window-based channel estimation for OFDM system in multi-path fast time-varying channels. IEICE Transactions Communications, E98-B(11), 2330–2340.
Jan, Y. H. (2015). Reduced-overhead channel estimation for OFDM systems in multipath fast time-varying channels. Journal of Internet Technology, 16(6), 1099–1108.
Yang, B., Letaief, K. B., Cheng, R. S., & Cao, Z. (2001). Channel estimation for OFDM transmission in multipath fading channel based on parametric channel modeling. IEEE Transactions on Communications, 49(3), 467–479.
Li, Y. (2000). Pilot-symbol-aided channel estimation for OFDM in wireless systems. IEEE Transactions on Vehicle Technology, 49(4), 1207–1215.
Li, Y., Cimini, L. J., & Sollenberger, N. R. (1998). Robust channel estimation for OFDM systems with rapid dispersive fading channels. IEEE Transactions on Communications, 46(7), 902–915.
Athaudage, C. R. N., & Jayalath, A. D. S. (2004). Delay-spread estimation using cyclic-prefix in wireless OFDM systems. IEE Proceedings Communications, 151(6), 559–566.
Song, W. G., & Lim, J. T. (2003). Pilot-symbol aided channel estimation for OFDM with fast fading channels. IEEE Transactions on Broadcasting, 49(4), 398–402.
Morelli, M., & Mengali, U. (2001). A comparison of pilot-aided channel estimation methods for OFDM systems. IEEE Transactions on Signal Processing, 49(12), 3065–3073.
Wang, X., & Liu, K. J. R. (2002). An adaptive channel estimation algorithm using time-frequency polynomial model for OFDM with fading multipath channels. EURASIP Journal on Applied Signal Processing, 2002(8), 818–830.
Tang, Z., Cannizzaro, R. C., Leus, G., & Banelli, P. (2007). Pilot-assisted time-varying channel estimation for OFDM systems. IEEE Transactions on Signal Processing, 55(5), 2226–2238.
Hrycak, T., Das, S., Matz, G., & Feichtinger, H. G. (2011). Practical estimation of rapidly varying channels for OFDM systems. IEEE Transactions on Communications, 59(11), 3040–3048.
Chang, M. X., & Hsieh, T. D. (2008). Detection of OFDM signals in fast-varying channels with low-density pilot symbols. IEEE Transactions on Vehicle Technology, 57(2), 859–872.
Zhao, Z., Cheng, X., Wen, M., Yang, L., & Jiao, B. (2015). Constructed data pilot-assisted channel estimators for mobile environments. IEEE Transactions on Intelligent Transportation Systems, 16(2), 947–957.
Liu, G., Zhidkov, S. V., Li, H., Zeng, L., & Wang, Z. (2012). Low- complexity iterative equalization for symbol-reconstruction-based OFDM receivers over doubly selective channels. IEEE Transactions on Broadcasting, 58(3), 390–400.
COST 207 Management Committee. (1989). COST 207: Digital land mobile radio communications. Brussels: Commission of the European Communication.
Roni, H. H., Wei, S. W., Jan, Y. H., Chen, T. C., & Wen, J. H. (2009). Low complexity qSIC equalizer for OFDM systems. In: Proceedings 13th IEEE International Symposium on Consumer Electronics (ISCE), Kyoto, Japan, 25–28, May 2009; 434–438.
Jeon, W. G., Chang, K. H., & Cho, Y. S. (1999). An equalization technique for orthogonal frequency-division multiplexing systems in time-variant multipath channels. IEEE Transactions on Communications, 47(1), 27–32.
Acknowledgements
This work was partially supported by the Ministry of Science and Technology of the Taiwan, R.O.C. (Grant No. NSC 103-2218-E-539 -001).
Author information
Authors and Affiliations
Corresponding author
Appendix A: q-Banded Successive Interference Suppression
Appendix A: q-Banded Successive Interference Suppression
Let H D and Y D be taken from the data subcarriers of H r and Y, respectively. The relation of H D and H r is H D (m,n) = H r (a,b) with a = κD + r + 1 and b = λD + ω + 1, in which κ = Int(m/(D − 1)); λ = Int(n/(D − 1)); r = (m) D − 1; ω = (n) D − 1; m = 0, 1, …, N – Np − 1; n = 0, 1, …, N − Np − 1. Similarly, for Y D (m) = Y(a), we have a = κD + r + 1, where κ = Int(m/(D − 1)), r = (m) D − 1, and m = 0, 1, …, N − Np − 1.
For convenience, the MATLAB-style notation A(i:j,k:l) is adopted to represent a submatrix of A with rows i through j and columns k through l. Furthermore, we use A(i,:) and A(:,j) to indicate the ith row and the jth column of A respectively. The term A(i:j,k) represents the column vector of A that comprising rows i through j at kth column of A, and likewise for the row vector A(k,i:j).
The qSIS method performs an iterative detection that initializes the detection process by identifying the subcarrier with strongest power first and leaving the remaining subcarrier to be detected in successive iterations. A list of detection sequence is required. This study employs subcarrier power to quantify the subcarrier quality. Let the power vector P D be denoted as:
Figure 14 depicts the flowchart of the proposed qSIS method. The ordered sequence K D is obtained from the sorted P D , where K D (i) = j means that the j-th subcarrier will be detected at the i-th iteration. For simplification, the superscript i indicates the detection at the i-th iteration, marked by X i D , from H i D and Y i D . For initialization, set i = 0, H 0 D = H D , and Y 0 D = Y D . Considering only q− banded dominant ICI terms in H i D , at the i-th iteration, the j-th subcarrier has the largest power, and its signal of this subcarrier can be estimated as follows:
where (A i) + r indicates the r- th row of (A i)+, and Y i q = Y i D (m:n)∈ ℂ(2q + 1) × 1 and A i = H i D (m:n,m:n)∈ ℂ(2q + 1)×(2q + 1), with
The estimated j-th subcarrier can then be rewritten into
After the j-th subcarrier is determined, the interference compensation is obtained as
Assuming that the j-th subcarrier interference has been eliminated in (33), the j-th column of channel matrix H i D should be updated by nulling the corresponding column. This iterative procedure should be performed until the signal of the last subcarrier is determined. The detailed operation of the presented qSIS algorithm is summarized as follows:
-
(0)
Set the initial channel matrix H 0 D = H D and Y 0 D = Y D ; set i = 0.
-
(1)
Determine the power vector P D to obtain an ordered sequence K D .
-
(2)
For j = K D (i), obtain A i and Y i q from H i D and Y i D , respectively.
-
(3)
Obtain X i D (j) from (29)
-
(4)
Calculate Y i+1 D from (33)
-
(5)
If i = N − N p − 1, then end the detection; otherwise, update the channel matrix H i+1 D by nulling the j-th column; then i = i + 1and go to step 2).
Finally, the qSIS output is given by:
where X (2) d ∈ ℂN × 1, k∈ Ω d , and m = 0,1,…, N− N p − 1.
Table 3 lists the signal detection for H D and the complexity comparison from ZF, SIS and the proposed qSIS is presented. As discussed in [26], the main drawback of conventional SIS detection is the iterative process. This study found that the conventional needs to perform matrix inversions of O((N − N p )2.376) for total (N − N p ) times. The computational complexity of conventional SIS is still impractical for real time systems, particularly with a large number of data subcarriers N − N p . For qSIS, the size of partial matrix A i in (29) is (2q + 1) × (2q + 1) reducing the required inverse operation to estimate j-th subcarrier’s signal is O((2q + 1)2.376). The qSIS detection is the iterative process that need to perform matrix inversions of O(N 2.376 ) for total N − N p times. Hence the complexity of the proposed qSIS is only (N − N p )(2q + 1)2.376. Figure 15 illustrates the flowchart of the proposed low- complexity qSIS method. ZF- only requires one matrix inverse operation with computation complexity of O((N − N p )2.376) [1] [27]. Therefore, the complexity gap between qSIS and SIS becomes more significant with larger N − N p . For case N = 512 and N p = 256, SIS is 673 times as complex as ZF. The qSIS (q = 5) only requires 14.4% of ZF complexity, and 0.021% of SIS complexity. The qSIS with q = 7 requires 30% of ZF complexity, and 0.045% of SIC complexity, while qSIS with q = 9 requires 0.079% of SIS complexity and is only 53% more complex than ZF.
Rights and permissions
About this article
Cite this article
Jan, YH. Fast Time-Varying Multipath Channel Estimation and Signal Detection for of OFDM Systems with Interference Compensation. Wireless Pers Commun 98, 2569–2590 (2018). https://doi.org/10.1007/s11277-017-4990-9
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11277-017-4990-9